Experiment with Random Difficulty (results based on the bet)
## Nombre de participants se déclarant comme joueurs : 29## Nombre de femmes se déclarant comme joueuses : 3## Age médian des joueurs : 15Removing Outliers based on BET
## [1] "Outliers BET STANDARD DEVIATION: 3qq8dp8jk, 79pn8m6v8, e58u3sinl, urgv6o806"
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers BET SAVED SHEEPS: "## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Outliers BET EXPLOIT DDA: vuq3c2tk6"## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 5"## [1] "Total number of outliers motor task: 2"## [1] "Total number of outliers perceptive task: 1"## [1] "Total number of outliers logical task: 2"Removing Outliers based on CONFIDENCE SCALE
## [1] "Outliers CS STANDARD DEVIATION: 9b3ph38yc, a6dfu5ljd, dyg7cga2o, tmxmxmwhi, zp9bc59o5"## Empty data.table (0 rows) of 1 col: IDjoueur## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur## [1] "Total number of outliers: 5"## [1] "Total number of outliers motor task: 0"## [1] "Total number of outliers perceptive task: 5"## [1] "Total number of outliers logical task: 0"Modeling difficulties
Modeling objective difficulty for motor task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1953.7 1975.3 -972.8 1945.7 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1396 -0.7500 0.2888 0.7385 2.8481
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.5631 0.7504
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.0298 0.1873 -5.499 3.83e-08 ***
## difficulty 2.9618 0.2146 13.803 < 2e-16 ***
## timeNorm -0.5280 0.2020 -2.614 0.00895 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.539
## timeNorm -0.571 -0.009## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 1624 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.050110
## 1st Qu.:-0.438217
## Median :-0.118832
## Mean :-0.002364
## 3rd Qu.: 0.296005
## Max. : 1.658440
## [1] "Intercept: -1.03 3.8e-08 ***"
## [1] "Difficulty: 2.96 2.4e-43 ***"
## [1] "Time: -0.528 0.009 **"
## [1] "R2 fixed: 0.16"
## [1] "R2 mixed: 0.29"
## [1] "Cross Val: 0.69"
## [1] "AIC: 2000"## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## 0% 25% 50% 75% 100%
## -1.6584395 -0.2960052 0.1188317 0.4382172 1.0501105
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for sensory task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1261.1 1282.7 -626.5 1253.1 1620
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.3943 -0.3586 0.1131 0.3536 6.6338
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.7241 0.8509
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.3288 0.2583 -12.885 <2e-16 ***
## difficulty 8.2778 0.4068 20.346 <2e-16 ***
## timeNorm -0.2933 0.2674 -1.097 0.273
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.650
## timeNorm -0.519 -0.046## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)## The result is correct only if all data used by the model has not changed since model was fitted.## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control
## $checkConv, : Model failed to converge with max|grad| = 2.21089 (tol =
## 0.001, component 1)## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 0 0 1624
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.6765404
## 1st Qu.:-0.4435738
## Median : 0.0778425
## Mean :-0.0007671
## 3rd Qu.: 0.4353921
## Max. : 1.5192471
## [1] "Intercept: -3.33 5.5e-38 ***"
## [1] "Difficulty: 8.28 5e-92 ***"
## [1] "Time: -0.293 0.27 :("
## [1] "R2 fixed: 0.34"
## [1] "R2 mixed: 0.44"
## [1] "Cross Val: 0.82"
## [1] "AIC: 1300"## 0% 25% 50% 75% 100%
## -1.51924712 -0.43539206 -0.07784249 0.44357377 1.67654045
## 0% 25% 50% 75% 100%
## -1.51924712 -0.43539206 -0.07784249 0.44357377 1.67654045
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Modeling objective difficulty for logical task
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
## Data: DT
##
## AIC BIC logLik deviance df.resid
## 1426.5 1447.8 -709.2 1418.5 1504
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9435 -0.5021 -0.1156 0.5089 4.9862
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 1.577 1.256
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.8650 0.2652 -7.033 2.01e-12 ***
## difficulty 5.6686 0.3206 17.680 < 2e-16 ***
## timeNorm -1.9313 0.2573 -7.507 6.04e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dffclt
## difficulty -0.496
## timeNorm -0.378 -0.227## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.##
## Logique2 Motrice Sensoriel
## 1508 0 0
## [1] "Player levels from ranef:"
## (Intercept)
## Min. :-1.7902825
## 1st Qu.:-0.7784485
## Median :-0.3355504
## Mean :-0.0003123
## 3rd Qu.: 0.7369882
## Max. : 3.1275697
## [1] "Intercept: -1.86 2e-12 ***"
## [1] "Difficulty: 5.67 6e-70 ***"
## [1] "Time: -1.93 6e-14 ***"
## [1] "R2 fixed: 0.38"
## [1] "R2 mixed: 0.58"
## [1] "Cross Val: 0.8"
## [1] "AIC: 1400"## 0% 25% 50% 75% 100%
## -3.1275697 -0.7369882 0.3355504 0.7784485 1.7902825
## 0% 25% 50% 75% 100%
## -3.1275697 -0.7369882 0.3355504 0.7784485 1.7902825
## `geom_smooth()` using method = 'gam'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
## `geom_smooth()` using method = 'loess'
Influence of Player Profiles
Player profiles
Influence of Player Profiles
Objective level and player profile
Playing video games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3815, p-value = 0.1671
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1442117
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.68759, p-value = 0.4917
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.07199342
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.30458, p-value = 0.7607
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03301126Playing board games in general and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.86453, p-value = 0.3873
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.08913015
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.48979, p-value = 0.6243
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.05061255
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.79975, p-value = 0.4239
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.08596507Self efficacy and level for each task
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.17852, p-value = 0.8583
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.02429648## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.4833, p-value = 0.01302
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.3393258
##
## [1] "self.eff.on.level.s 0.34 0.013 *"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 26 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.51036, p-value = 0.6098
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07281435Risk aversion and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.5679, p-value = 0.1169
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1554335
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.1214, p-value = 0.03389
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2101231
##
## [1] "risk.av.on.level.s 0.21 0.034 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.3062, p-value = 0.1915
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1347244Age and level for each task
## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.97478, p-value = 0.3297
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.09369113## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.2162, p-value = 0.02668
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2137687
##
## [1] "age.on.level.s 0.21 0.027 *"## Warning: Removed 1 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.2774, p-value = 0.2015
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1275074Sex and level for each task
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.1404, p-value = 0.03233
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2377395
##
## [1] "sexe.on.level.m -0.24 0.032 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.077873, p-value = 0.9379
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.008649769
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.26928, p-value = 0.7877
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03108211
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 220, p-value = 0.03213
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.82775747 -0.05457213
## sample estimates:
## difference in location
## -0.4558716
##
## [1] "sexe.on.level.m.2 -0.46 0.032 * mean(A): 0.15 mean(B): -0.31"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 347, p-value = 0.9453
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.4361429 0.4780691
## sample estimates:
## difference in location
## -0.01100307
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 292, p-value = 0.7971
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.8271570 0.5994594
## sample estimates:
## difference in location
## -0.04046848CONFIDENCE SCALE APPROACH
For Bet approach, see the other file.
Influence of Objective difficulty on Subjective Difficulty
All tasks
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.130 49 7.6e-05 ***
## 2: 0.09375 0.160 57 1.3e-05 ***
## 3: 0.15625 0.110 57 0.00037 ***
## 4: 0.21875 0.140 57 2.8e-06 ***
## 5: 0.28125 0.110 58 0.00028 ***
## 6: 0.34375 0.110 57 1.5e-06 ***
## 7: 0.40625 0.069 56 0.013 *
## 8: 0.46875 0.015 58 0.42 :(
## 9: 0.53125 -0.031 56 0.13 :(
## 10: 0.59375 -0.044 58 0.056 .
## 11: 0.65625 -0.110 58 5.4e-05 ***
## 12: 0.71875 -0.130 58 2.4e-06 ***
## 13: 0.78125 -0.190 57 1.8e-08 ***
## 14: 0.84375 -0.230 55 3.7e-09 ***
## 15: 0.90625 -0.240 57 1.2e-10 ***
## 16: 0.96875 -0.190 57 9.9e-10 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 49 7.6e-05 ***
## 2: 57 1.3e-05 ***
## 3: 57 0.00037 ***
## 4: 57 2.8e-06 ***
## 5: 58 0.00028 ***
## 6: 57 1.5e-06 ***
## 7: 56 0.013 *
## 8: 58 0.42 :(
## 9: 56 0.13 :(
## 10: 58 0.056 .
## 11: 58 5.4e-05 ***
## 12: 58 2.4e-06 ***
## 13: 57 1.8e-08 ***
## 14: 55 3.7e-09 ***
## 15: 57 1.2e-10 ***
## 16: 57 9.9e-10 ***
## [1] 56.6
## [1] 2.19
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.074 34 0.0093 **
## 2: 0.09375 0.110 36 0.011 *
## 3: 0.15625 0.094 42 0.011 *
## 4: 0.21875 0.130 40 0.00017 ***
## 5: 0.28125 0.110 38 0.0028 **
## 6: 0.34375 0.120 38 0.00027 ***
## 7: 0.40625 0.077 40 0.022 *
## 8: 0.46875 0.031 38 0.22 :(
## 9: 0.53125 -0.031 37 0.35 :(
## 10: 0.59375 -0.058 40 0.09 .
## 11: 0.65625 -0.098 36 0.027 *
## 12: 0.71875 -0.180 37 1.5e-05 ***
## 13: 0.78125 -0.180 38 6.7e-05 ***
## 14: 0.84375 -0.240 25 0.00013 ***
## 15: 0.90625 -0.260 29 1e-05 ***
## 16: 0.96875 -0.180 19 0.003 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 34 0.0093 **
## 2: 36 0.011 *
## 3: 42 0.011 *
## 4: 40 0.00017 ***
## 5: 38 0.0028 **
## 6: 38 0.00027 ***
## 7: 40 0.022 *
## 8: 38 0.22 :(
## 9: 37 0.35 :(
## 10: 40 0.09 .
## 11: 36 0.027 *
## 12: 37 1.5e-05 ***
## 13: 38 6.7e-05 ***
## 14: 25 0.00013 ***
## 15: 29 1e-05 ***
## 16: 19 0.003 **
## [1] 35.4
## [1] 6.11
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.140 28 0.0017 **
## 2: 0.09375 0.170 32 0.00031 ***
## 3: 0.15625 0.120 29 0.0086 **
## 4: 0.21875 0.130 36 0.0031 **
## 5: 0.28125 0.094 33 0.12 :(
## 6: 0.34375 0.110 36 0.033 *
## 7: 0.40625 0.036 36 0.55 :(
## 8: 0.46875 -0.010 34 0.78 :(
## 9: 0.53125 -0.029 35 0.71 :(
## 10: 0.59375 -0.054 33 0.42 :(
## 11: 0.65625 -0.160 36 0.00019 ***
## 12: 0.71875 -0.110 37 0.0026 **
## 13: 0.78125 -0.150 37 0.00012 ***
## 14: 0.84375 -0.210 36 1e-05 ***
## 15: 0.90625 -0.230 33 2.8e-06 ***
## 16: 0.96875 -0.150 31 2.6e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 28 0.0017 **
## 2: 32 0.00031 ***
## 3: 29 0.0086 **
## 4: 36 0.0031 **
## 5: 33 0.12 :(
## 6: 36 0.033 *
## 7: 36 0.55 :(
## 8: 34 0.78 :(
## 9: 35 0.71 :(
## 10: 33 0.42 :(
## 11: 36 0.00019 ***
## 12: 37 0.0026 **
## 13: 37 0.00012 ***
## 14: 36 1e-05 ***
## 15: 33 2.8e-06 ***
## 16: 31 2.6e-05 ***
## [1] 33.9
## [1] 2.78
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.081 10 0.21 :(
## 3: 0.15625 0.190 12 0.036 *
## 4: 0.21875 0.031 12 0.29 :(
## 5: 0.28125 0.220 13 0.0061 **
## 6: 0.34375 0.160 12 0.0044 **
## 7: 0.40625 0.160 14 0.059 .
## 8: 0.46875 0.031 16 0.15 :(
## 9: 0.53125 -0.031 16 0.029 *
## 10: 0.59375 0.021 16 0.9 :(
## 11: 0.65625 -0.031 16 0.62 :(
## 12: 0.71875 -0.062 16 0.11 :(
## 13: 0.78125 -0.180 18 0.0076 **
## 14: 0.84375 -0.240 20 0.00045 ***
## 15: 0.90625 -0.230 20 0.00022 ***
## 16: 0.96875 -0.310 20 9.3e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 10 0.21 :(
## 2: 12 0.036 *
## 3: 12 0.29 :(
## 4: 13 0.0061 **
## 5: 12 0.0044 **
## 6: 14 0.059 .
## 7: 16 0.15 :(
## 8: 16 0.029 *
## 9: 16 0.9 :(
## 10: 16 0.62 :(
## 11: 16 0.11 :(
## 12: 18 0.0076 **
## 13: 20 0.00045 ***
## 14: 20 0.00022 ***
## 15: 20 9.3e-05 ***
## [1] 15.4
## [1] 3.2## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Motor task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.120 8 0.44 :(
## 3: 0.15625 0.094 26 0.44 :(
## 4: 0.21875 0.067 40 0.052 .
## 5: 0.28125 0.069 45 0.09 .
## 6: 0.34375 0.110 47 0.013 *
## 7: 0.40625 0.064 49 0.091 .
## 8: 0.46875 0.048 49 0.045 *
## 9: 0.53125 0.019 51 0.56 :(
## 10: 0.59375 -0.044 51 0.41 :(
## 11: 0.65625 -0.090 53 0.0052 **
## 12: 0.71875 -0.069 51 0.0016 **
## 13: 0.78125 -0.110 44 0.00056 ***
## 14: 0.84375 -0.170 27 0.0029 **
## 15: 0.90625 -0.210 14 0.0094 **
## 16: 0.96875 -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.44 :(
## 2: 26 0.44 :(
## 3: 40 0.052 .
## 4: 45 0.09 .
## 5: 47 0.013 *
## 6: 49 0.091 .
## 7: 49 0.045 *
## 8: 51 0.56 :(
## 9: 51 0.41 :(
## 10: 53 0.0052 **
## 11: 51 0.0016 **
## 12: 44 0.00056 ***
## 13: 27 0.0029 **
## 14: 14 0.0094 **
## 15: 6 0.056 .
## [1] 37.4
## [1] 16.7## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.1200 8 0.44 :(
## 3: 0.15625 0.0770 24 0.52 :(
## 4: 0.21875 0.0680 26 0.13 :(
## 5: 0.28125 0.1100 25 0.029 *
## 6: 0.34375 0.1100 26 0.0021 **
## 7: 0.40625 0.0940 25 0.036 *
## 8: 0.46875 0.1100 24 0.0068 **
## 9: 0.53125 0.0690 23 0.37 :(
## 10: 0.59375 0.0410 24 0.62 :(
## 11: 0.65625 -0.0063 25 0.4 :(
## 12: 0.71875 -0.0690 22 0.054 .
## 13: 0.78125 -0.0810 15 0.042 *
## 14: 0.84375 NA 0 NA
## 15: 0.90625 NA 0 NA
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.44 :(
## 2: 24 0.52 :(
## 3: 26 0.13 :(
## 4: 25 0.029 *
## 5: 26 0.0021 **
## 6: 25 0.036 *
## 7: 24 0.0068 **
## 8: 23 0.37 :(
## 9: 24 0.62 :(
## 10: 25 0.4 :(
## 11: 22 0.054 .
## 12: 15 0.042 *
## [1] 22.2
## [1] 5.36## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 0.290 2 1 :(
## 4: 0.21875 0.073 14 0.23 :(
## 5: 0.28125 0.035 20 0.64 :(
## 6: 0.34375 0.044 20 0.81 :(
## 7: 0.40625 0.014 22 0.95 :(
## 8: 0.46875 -0.019 21 0.68 :(
## 9: 0.53125 0.019 21 0.53 :(
## 10: 0.59375 -0.094 21 0.06 .
## 11: 0.65625 -0.160 21 0.0065 **
## 12: 0.71875 -0.069 22 0.066 .
## 13: 0.78125 -0.081 21 0.07 .
## 14: 0.84375 -0.180 19 0.016 *
## 15: 0.90625 -0.230 6 0.093 .
## 16: 0.96875 NA 0 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 1 :(
## 2: 14 0.23 :(
## 3: 20 0.64 :(
## 4: 20 0.81 :(
## 5: 22 0.95 :(
## 6: 21 0.68 :(
## 7: 21 0.53 :(
## 8: 21 0.06 .
## 9: 21 0.0065 **
## 10: 22 0.066 .
## 11: 21 0.07 .
## 12: 19 0.016 *
## 13: 6 0.093 .
## [1] 17.7
## [1] 6.46## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 0 NA
## 3: 0.15625 NA 0 NA
## 4: 0.21875 NA 0 NA
## 5: 0.28125 NA 0 NA
## 6: 0.34375 NA 1 NA
## 7: 0.40625 0.190 2 0.5 :(
## 8: 0.46875 NA 4 NA
## 9: 0.53125 -0.031 7 0.19 :(
## 10: 0.59375 -0.044 6 0.52 :(
## 11: 0.65625 -0.160 7 0.33 :(
## 12: 0.71875 -0.100 7 0.14 :(
## 13: 0.78125 -0.180 8 0.028 *
## 14: 0.84375 -0.160 8 0.1 :(
## 15: 0.90625 -0.210 8 0.055 .
## 16: 0.96875 -0.270 6 0.056 .
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 2 0.5 :(
## 2: 7 0.19 :(
## 3: 6 0.52 :(
## 4: 7 0.33 :(
## 5: 7 0.14 :(
## 6: 8 0.028 *
## 7: 8 0.1 :(
## 8: 8 0.055 .
## 9: 6 0.056 .
## [1] 6.56
## [1] 1.88## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_errorbar).
Sensory task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.085 44 0.0094 **
## 2: 0.09375 0.140 53 0.0041 **
## 3: 0.15625 0.094 48 0.081 .
## 4: 0.21875 0.048 40 0.069 .
## 5: 0.28125 0.019 38 0.59 :(
## 6: 0.34375 0.056 36 0.43 :(
## 7: 0.40625 -0.031 37 0.42 :(
## 8: 0.46875 -0.120 37 0.041 *
## 9: 0.53125 -0.210 30 0.00093 ***
## 10: 0.59375 -0.094 33 0.014 *
## 11: 0.65625 -0.160 34 8.1e-05 ***
## 12: 0.71875 -0.220 34 0.00014 ***
## 13: 0.78125 -0.280 38 7.5e-07 ***
## 14: 0.84375 -0.270 45 8.4e-07 ***
## 15: 0.90625 -0.230 53 3.5e-09 ***
## 16: 0.96875 -0.150 56 4e-08 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 44 0.0094 **
## 2: 53 0.0041 **
## 3: 48 0.081 .
## 4: 40 0.069 .
## 5: 38 0.59 :(
## 6: 36 0.43 :(
## 7: 37 0.42 :(
## 8: 37 0.041 *
## 9: 30 0.00093 ***
## 10: 33 0.014 *
## 11: 34 8.1e-05 ***
## 12: 34 0.00014 ***
## 13: 38 7.5e-07 ***
## 14: 45 8.4e-07 ***
## 15: 53 3.5e-09 ***
## 16: 56 4e-08 ***
## [1] 41
## [1] 7.94
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.084 19 0.1 :(
## 2: 0.09375 0.031 18 0.72 :(
## 3: 0.15625 0.094 17 0.31 :(
## 4: 0.21875 0.081 10 0.26 :(
## 5: 0.28125 0.052 16 0.45 :(
## 6: 0.34375 -0.044 12 0.14 :(
## 7: 0.40625 -0.160 12 0.024 *
## 8: 0.46875 -0.170 15 0.018 *
## 9: 0.53125 -0.280 11 0.018 *
## 10: 0.59375 -0.290 12 0.01 *
## 11: 0.65625 -0.280 12 0.0022 **
## 12: 0.71875 -0.340 11 0.0033 **
## 13: 0.78125 -0.280 12 0.0081 **
## 14: 0.84375 -0.340 13 0.0039 **
## 15: 0.90625 -0.230 18 0.00048 ***
## 16: 0.96875 -0.170 19 0.0065 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 19 0.1 :(
## 2: 18 0.72 :(
## 3: 17 0.31 :(
## 4: 10 0.26 :(
## 5: 16 0.45 :(
## 6: 12 0.14 :(
## 7: 12 0.024 *
## 8: 15 0.018 *
## 9: 11 0.018 *
## 10: 12 0.01 *
## 11: 12 0.0022 **
## 12: 11 0.0033 **
## 13: 12 0.0081 **
## 14: 13 0.0039 **
## 15: 18 0.00048 ***
## 16: 19 0.0065 **
## [1] 14.2
## [1] 3.17
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.0850 25 0.052 .
## 2: 0.09375 0.1600 27 0.012 *
## 3: 0.15625 -0.0062 21 0.97 :(
## 4: 0.21875 0.0310 22 0.24 :(
## 5: 0.28125 -0.0310 15 0.71 :(
## 6: 0.34375 0.0730 19 0.059 .
## 7: 0.40625 0.0380 20 0.87 :(
## 8: 0.46875 -0.0440 17 0.81 :(
## 9: 0.53125 -0.1100 15 0.091 .
## 10: 0.59375 -0.0690 16 0.45 :(
## 11: 0.65625 -0.1600 17 0.017 *
## 12: 0.71875 -0.1200 16 0.03 *
## 13: 0.78125 -0.2300 21 0.00041 ***
## 14: 0.84375 -0.2400 24 0.00092 ***
## 15: 0.90625 -0.2100 27 5e-05 ***
## 16: 0.96875 -0.0800 27 0.00018 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 25 0.052 .
## 2: 27 0.012 *
## 3: 21 0.97 :(
## 4: 22 0.24 :(
## 5: 15 0.71 :(
## 6: 19 0.059 .
## 7: 20 0.87 :(
## 8: 17 0.81 :(
## 9: 15 0.091 .
## 10: 16 0.45 :(
## 11: 17 0.017 *
## 12: 16 0.03 *
## 13: 21 0.00041 ***
## 14: 24 0.00092 ***
## 15: 27 5e-05 ***
## 16: 27 0.00018 ***
## [1] 20.6
## [1] 4.4
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 0.140 8 0.1 :(
## 3: 0.15625 0.190 10 0.04 *
## 4: 0.21875 0.070 8 0.44 :(
## 5: 0.28125 0.094 7 0.44 :(
## 6: 0.34375 0.081 5 0.18 :(
## 7: 0.40625 0.110 5 0.44 :(
## 8: 0.46875 -0.120 5 0.78 :(
## 9: 0.53125 -0.260 4 0.12 :(
## 10: 0.59375 -0.094 5 0.58 :(
## 11: 0.65625 -0.160 5 0.41 :(
## 12: 0.71875 -0.074 7 0.55 :(
## 13: 0.78125 -0.280 5 0.054 .
## 14: 0.84375 -0.220 8 0.041 *
## 15: 0.90625 -0.290 8 0.014 *
## 16: 0.96875 -0.240 10 0.0059 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 8 0.1 :(
## 2: 10 0.04 *
## 3: 8 0.44 :(
## 4: 7 0.44 :(
## 5: 5 0.18 :(
## 6: 5 0.44 :(
## 7: 5 0.78 :(
## 8: 4 0.12 :(
## 9: 5 0.58 :(
## 10: 5 0.41 :(
## 11: 7 0.55 :(
## 12: 5 0.054 .
## 13: 8 0.041 *
## 14: 8 0.014 *
## 15: 10 0.0059 **
## [1] 6.67
## [1] 1.95## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
Logical task
## [1] "all"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.089 35 0.017 *
## 2: 0.09375 0.160 40 5.2e-05 ***
## 3: 0.15625 0.150 40 0.00025 ***
## 4: 0.21875 0.230 42 9.5e-06 ***
## 5: 0.28125 0.220 34 0.00028 ***
## 6: 0.34375 0.160 39 5.5e-05 ***
## 7: 0.40625 0.094 44 0.011 *
## 8: 0.46875 0.031 39 0.024 *
## 9: 0.53125 -0.031 37 0.21 :(
## 10: 0.59375 -0.019 41 0.77 :(
## 11: 0.65625 -0.018 39 0.68 :(
## 12: 0.71875 -0.100 38 0.002 **
## 13: 0.78125 -0.160 43 9.5e-05 ***
## 14: 0.84375 -0.220 41 6.5e-07 ***
## 15: 0.90625 -0.260 40 3.4e-07 ***
## 16: 0.96875 -0.340 25 1.4e-05 ***
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 35 0.017 *
## 2: 40 5.2e-05 ***
## 3: 40 0.00025 ***
## 4: 42 9.5e-06 ***
## 5: 34 0.00028 ***
## 6: 39 5.5e-05 ***
## 7: 44 0.011 *
## 8: 39 0.024 *
## 9: 37 0.21 :(
## 10: 41 0.77 :(
## 11: 39 0.68 :(
## 12: 38 0.002 **
## 13: 43 9.5e-05 ***
## 14: 41 6.5e-07 ***
## 15: 40 3.4e-07 ***
## 16: 25 1.4e-05 ***
## [1] 38.6
## [1] 4.47
## [1] "good"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.050 26 0.071 .
## 2: 0.09375 0.110 26 0.007 **
## 3: 0.15625 0.110 24 0.027 *
## 4: 0.21875 0.200 24 0.0014 **
## 5: 0.28125 0.140 17 0.13 :(
## 6: 0.34375 0.160 21 0.036 *
## 7: 0.40625 0.120 22 0.085 .
## 8: 0.46875 0.031 20 0.15 :(
## 9: 0.53125 -0.031 18 0.27 :(
## 10: 0.59375 -0.094 21 0.19 :(
## 11: 0.65625 -0.056 17 0.57 :(
## 12: 0.71875 -0.120 18 0.026 *
## 13: 0.78125 -0.160 21 0.0081 **
## 14: 0.84375 -0.220 18 0.0018 **
## 15: 0.90625 -0.310 15 0.0024 **
## 16: 0.96875 NA 1 NA
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 26 0.071 .
## 2: 26 0.007 **
## 3: 24 0.027 *
## 4: 24 0.0014 **
## 5: 17 0.13 :(
## 6: 21 0.036 *
## 7: 22 0.085 .
## 8: 20 0.15 :(
## 9: 18 0.27 :(
## 10: 21 0.19 :(
## 11: 17 0.57 :(
## 12: 18 0.026 *
## 13: 21 0.0081 **
## 14: 18 0.0018 **
## 15: 15 0.0024 **
## [1] 20.5
## [1] 3.4## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_errorbar).
## [1] "medium"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 0.140 9 0.15 :(
## 2: 0.09375 0.360 12 0.0031 **
## 3: 0.15625 0.340 13 0.0026 **
## 4: 0.21875 0.330 13 0.0031 **
## 5: 0.28125 0.220 10 0.0056 **
## 6: 0.34375 0.160 10 0.0067 **
## 7: 0.40625 0.094 13 0.35 :(
## 8: 0.46875 0.031 11 0.049 *
## 9: 0.53125 -0.031 10 0.75 :(
## 10: 0.59375 0.031 10 0.36 :(
## 11: 0.65625 -0.110 12 0.12 :(
## 12: 0.71875 -0.220 12 0.053 .
## 13: 0.78125 -0.240 13 0.0039 **
## 14: 0.84375 -0.220 13 0.0026 **
## 15: 0.90625 -0.250 13 0.0032 **
## 16: 0.96875 -0.340 12 0.0031 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 9 0.15 :(
## 2: 12 0.0031 **
## 3: 13 0.0026 **
## 4: 13 0.0031 **
## 5: 10 0.0056 **
## 6: 10 0.0067 **
## 7: 13 0.35 :(
## 8: 11 0.049 *
## 9: 10 0.75 :(
## 10: 10 0.36 :(
## 11: 12 0.12 :(
## 12: 12 0.053 .
## 13: 13 0.0039 **
## 14: 13 0.0026 **
## 15: 13 0.0032 **
## 16: 12 0.0031 **
## [1] 11.6
## [1] 1.41
## [1] "bad"## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## obj.diff.bin delta.obj.subj n pval
## 1: 0.03125 NA 0 NA
## 2: 0.09375 NA 2 NA
## 3: 0.15625 NA 3 NA
## 4: 0.21875 0.031 5 0.59 :(
## 5: 0.28125 0.450 7 0.034 *
## 6: 0.34375 0.270 8 0.019 *
## 7: 0.40625 0.240 9 0.096 .
## 8: 0.46875 0.120 8 0.53 :(
## 9: 0.53125 -0.031 9 0.55 :(
## 10: 0.59375 0.031 10 0.68 :(
## 11: 0.65625 0.069 10 0.22 :(
## 12: 0.71875 -0.056 8 0.44 :(
## 13: 0.78125 -0.031 9 0.55 :(
## 14: 0.84375 -0.220 10 0.014 *
## 15: 0.90625 -0.240 12 0.0052 **
## 16: 0.96875 -0.350 12 0.0025 **
## [1] "mean and sd of nb players per bin"
## nb pval
## 1: 5 0.59 :(
## 2: 7 0.034 *
## 3: 8 0.019 *
## 4: 9 0.096 .
## 5: 8 0.53 :(
## 6: 9 0.55 :(
## 7: 10 0.68 :(
## 8: 10 0.22 :(
## 9: 8 0.44 :(
## 10: 9 0.55 :(
## 11: 10 0.014 *
## 12: 12 0.0052 **
## 13: 12 0.0025 **
## [1] 9
## [1] 1.91## Warning: Removed 3 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_errorbar).
Influence of Playtime on Subjective Difficulty Error
For all groups, motor, sensitive and logical
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70319 -0.16766 0.00799 0.17682 0.64502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17379 0.01995 8.711 <2e-16 ***
## timeNorm 0.00431 0.02101 0.205 0.837
## obj.diff -0.37273 0.02619 -14.234 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05419176)
##
## Null deviance: 99.193 on 1623 degrees of freedom
## Residual deviance: 87.845 on 1621 degrees of freedom
## AIC: -120.62
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.77933 -0.20089 -0.03724 0.24111 0.77727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.10795 0.01735 6.220 6.3e-10 ***
## timeNorm 0.03878 0.02320 1.672 0.0948 .
## obj.diff -0.36404 0.01795 -20.278 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06798547)
##
## Null deviance: 138.38 on 1623 degrees of freedom
## Residual deviance: 110.20 on 1621 degrees of freedom
## AIC: 247.65
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74305 -0.21400 -0.02148 0.20096 0.71922
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20615 0.02036 10.127 < 2e-16 ***
## timeNorm 0.06739 0.02531 2.662 0.00785 **
## obj.diff -0.51720 0.02162 -23.927 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07044787)
##
## Null deviance: 151.98 on 1507 degrees of freedom
## Residual deviance: 106.02 on 1505 degrees of freedom
## AIC: 283.97
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5348214 0.6008109 -0.0627956484 112 0.021 *
## 2: 4.5 0.5291667 0.5714407 -0.0363491910 168 0.071 .
## 3: 7.5 0.5071429 0.5416953 -0.0317522384 168 0.12 :(
## 4: 10.5 0.5339286 0.5401276 0.0027661467 168 0.89 :(
## 5: 13.5 0.5071429 0.5174551 -0.0066784780 168 0.74 :(
## 6: 16.5 0.5232143 0.5305272 -0.0054376495 168 0.78 :(
## 7: 19.5 0.4976190 0.5315528 -0.0349803686 168 0.062 .
## 8: 22.5 0.4779762 0.4897264 -0.0103383643 168 0.64 :(
## 9: 25.5 0.4797619 0.4805683 0.0009212402 168 0.95 :(
## 10: 28.5 0.4642857 0.4572889 0.0071690193 168 0.72 :(
## time error.diff shapes
## 1: 1.5 -0.0627956484 24
## 2: 4.5 -0.0363491910 16
## 3: 7.5 -0.0317522384 16
## 4: 10.5 0.0027661467 16
## 5: 13.5 -0.0066784780 16
## 6: 16.5 -0.0054376495 16
## 7: 19.5 -0.0349803686 16
## 8: 22.5 -0.0103383643 16
## 9: 25.5 0.0009212402 16
## 10: 28.5 0.0071690193 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4696429 0.5941293 -0.13598057 112 2.7e-05 ***
## 2: 4.5 0.5125000 0.6104788 -0.08536726 168 5.7e-06 ***
## 3: 7.5 0.4666667 0.5299114 -0.06382281 168 0.0032 **
## 4: 10.5 0.5148810 0.5824635 -0.06568890 168 0.0015 **
## 5: 13.5 0.4773810 0.5656294 -0.08101223 168 1.6e-05 ***
## 6: 16.5 0.4345238 0.5333505 -0.10807690 168 6.4e-06 ***
## 7: 19.5 0.4875000 0.5641391 -0.06577197 168 0.00038 ***
## 8: 22.5 0.4976190 0.5656705 -0.05806955 168 0.003 **
## 9: 25.5 0.5392857 0.5874740 -0.03434793 168 0.06 .
## 10: 28.5 0.5017857 0.5711020 -0.06820755 168 0.0022 **
## time error.diff shapes
## 1: 1.5 -0.13598057 24
## 2: 4.5 -0.08536726 24
## 3: 7.5 -0.06382281 24
## 4: 10.5 -0.06568890 24
## 5: 13.5 -0.08101223 24
## 6: 16.5 -0.10807690 24
## 7: 19.5 -0.06577197 24
## 8: 22.5 -0.05806955 24
## 9: 25.5 -0.03434793 16
## 10: 28.5 -0.06820755 24
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4355769 0.5969130 -0.167868594 104 3.2e-06 ***
## 2: 4.5 0.5089744 0.6297636 -0.133783305 156 3.6e-06 ***
## 3: 7.5 0.5102564 0.5544687 -0.055654906 156 0.036 *
## 4: 10.5 0.5224359 0.5229882 -0.002890341 156 0.89 :(
## 5: 13.5 0.5173077 0.5312208 -0.020469231 156 0.44 :(
## 6: 16.5 0.5102564 0.5008164 0.003037161 156 0.91 :(
## 7: 19.5 0.4576923 0.4456698 0.001732469 156 0.95 :(
## 8: 22.5 0.4211538 0.4198655 -0.005262489 156 0.84 :(
## 9: 25.5 0.4576923 0.3963862 0.067707055 156 0.015 *
## 10: 28.5 0.4435897 0.3637653 0.061919707 156 0.014 *
## time error.diff shapes
## 1: 1.5 -0.167868594 24
## 2: 4.5 -0.133783305 24
## 3: 7.5 -0.055654906 24
## 4: 10.5 -0.002890341 16
## 5: 13.5 -0.020469231 16
## 6: 16.5 0.003037161 16
## 7: 19.5 0.001732469 16
## 8: 22.5 -0.005262489 16
## 9: 25.5 0.067707055 24
## 10: 28.5 0.061919707 24
For all taks, per group
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74490 -0.17535 -0.06458 0.23455 0.57334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.28603 0.03171 9.021 < 2e-16 ***
## timeNorm 0.08066 0.03043 2.650 0.00819 **
## obj.diff -0.61786 0.03250 -19.010 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06159923)
##
## Null deviance: 77.326 on 869 degrees of freedom
## Residual deviance: 53.407 on 867 degrees of freedom
## AIC: 49.166
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.74652 -0.21532 0.00706 0.22454 0.74696
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.18368 0.01831 10.033 <2e-16 ***
## timeNorm 0.03187 0.02201 1.448 0.148
## obj.diff -0.42661 0.02054 -20.770 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06803624)
##
## Null deviance: 154.38 on 1826 degrees of freedom
## Residual deviance: 124.10 on 1824 degrees of freedom
## AIC: 279.34
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTAll[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.7579 -0.1902 -0.0057 0.2034 0.7204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.13887 0.01539 9.024 <2e-16 ***
## timeNorm 0.03309 0.01996 1.658 0.0974 .
## obj.diff -0.37629 0.01927 -19.528 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06202091)
##
## Null deviance: 152.70 on 2058 degrees of freedom
## Residual deviance: 127.51 on 2056 degrees of freedom
## AIC: 123.58
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5550000 0.7875694 -0.24249236 60 1e-07 ***
## 2: 4.5 0.5711111 0.7762393 -0.22395064 90 8.2e-08 ***
## 3: 7.5 0.6122222 0.7666760 -0.17059578 90 3.5e-06 ***
## 4: 10.5 0.6355556 0.7322887 -0.10318972 90 0.0031 **
## 5: 13.5 0.6277778 0.7646042 -0.16703095 90 1.3e-05 ***
## 6: 16.5 0.6155556 0.7327936 -0.13630021 90 0.00022 ***
## 7: 19.5 0.6311111 0.7168309 -0.09544489 90 0.0014 **
## 8: 22.5 0.6188889 0.7273818 -0.11152008 90 0.0023 **
## 9: 25.5 0.6011111 0.6908376 -0.08505521 90 0.019 *
## 10: 28.5 0.6100000 0.6655627 -0.05175360 90 0.12 :(
## time error.diff shapes
## 1: 1.5 -0.24249236 24
## 2: 4.5 -0.22395064 24
## 3: 7.5 -0.17059578 24
## 4: 10.5 -0.10318972 24
## 5: 13.5 -0.16703095 24
## 6: 16.5 -0.13630021 24
## 7: 19.5 -0.09544489 24
## 8: 22.5 -0.11152008 24
## 9: 25.5 -0.08505521 24
## 10: 28.5 -0.05175360 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5055556 0.6004377 -0.096794693 126 0.0012 **
## 2: 4.5 0.5693122 0.6650662 -0.091152068 189 1.2e-05 ***
## 3: 7.5 0.5095238 0.5257523 -0.023100429 189 0.28 :(
## 4: 10.5 0.5455026 0.5759928 -0.028141123 189 0.23 :(
## 5: 13.5 0.5301587 0.5697923 -0.037004401 189 0.049 *
## 6: 16.5 0.5164021 0.5456342 -0.033848317 189 0.11 :(
## 7: 19.5 0.4957672 0.5590654 -0.065529105 189 0.002 **
## 8: 22.5 0.4862434 0.5124916 -0.031883747 189 0.14 :(
## 9: 25.5 0.5333333 0.5246238 0.003500624 189 0.88 :(
## 10: 28.5 0.5074074 0.5116642 -0.013045432 189 0.56 :(
## time error.diff shapes
## 1: 1.5 -0.096794693 24
## 2: 4.5 -0.091152068 24
## 3: 7.5 -0.023100429 16
## 4: 10.5 -0.028141123 16
## 5: 13.5 -0.037004401 24
## 6: 16.5 -0.033848317 16
## 7: 19.5 -0.065529105 24
## 8: 22.5 -0.031883747 16
## 9: 25.5 0.003500624 16
## 10: 28.5 -0.013045432 16
## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4281690 0.5141051 -0.080402452 142 0.0021 **
## 2: 4.5 0.4478873 0.4753359 -0.027143114 213 0.16 :(
## 3: 7.5 0.4309859 0.4608404 -0.028485150 213 0.14 :(
## 4: 10.5 0.4572770 0.4479476 0.013162796 213 0.49 :(
## 5: 13.5 0.4197183 0.4146644 0.010568298 213 0.6 :(
## 6: 16.5 0.4107981 0.4121246 -0.002777196 213 0.9 :(
## 7: 19.5 0.4056338 0.3916553 0.010544404 213 0.59 :(
## 8: 22.5 0.3849765 0.3778424 0.005899965 213 0.72 :(
## 9: 25.5 0.4117371 0.3752961 0.035408215 213 0.038 *
## 10: 28.5 0.3788732 0.3423094 0.026264248 213 0.17 :(
## time error.diff shapes
## 1: 1.5 -0.080402452 24
## 2: 4.5 -0.027143114 16
## 3: 7.5 -0.028485150 16
## 4: 10.5 0.013162796 16
## 5: 13.5 0.010568298 16
## 6: 16.5 -0.002777196 16
## 7: 19.5 0.010544404 16
## 8: 22.5 0.005899965 16
## 9: 25.5 0.035408215 24
## 10: 28.5 0.026264248 16
Per group, motor task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65062 -0.16552 -0.07705 0.21881 0.38387
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.29158 0.07860 3.710 0.00026 ***
## timeNorm 0.04078 0.04734 0.861 0.38990
## obj.diff -0.58583 0.08967 -6.533 4.12e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.03968196)
##
## Null deviance: 10.9054 on 231 degrees of freedom
## Residual deviance: 9.0872 on 229 degrees of freedom
## AIC: -85.263
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.6250000 0.8544830 -0.23067342 16 0.0013 **
## 2: 4.5 0.6375000 0.7995145 -0.16957820 24 0.0048 **
## 3: 7.5 0.6208333 0.7551085 -0.13379284 24 0.012 *
## 4: 10.5 0.6375000 0.7836615 -0.15718140 24 0.0079 **
## 5: 13.5 0.6250000 0.8240112 -0.20576489 24 6.4e-05 ***
## 6: 16.5 0.6375000 0.7818411 -0.15147782 24 0.027 *
## 7: 19.5 0.6541667 0.7263256 -0.07096924 24 0.13 :(
## 8: 22.5 0.6458333 0.7654436 -0.12523757 24 0.046 *
## 9: 25.5 0.6583333 0.7908307 -0.13301969 24 0.0072 **
## 10: 28.5 0.6166667 0.7394768 -0.11097038 24 0.039 *
## time error.diff shapes
## 1: 1.5 -0.23067342 24
## 2: 4.5 -0.16957820 24
## 3: 7.5 -0.13379284 24
## 4: 10.5 -0.15718140 24
## 5: 13.5 -0.20576489 24
## 6: 16.5 -0.15147782 24
## 7: 19.5 -0.07096924 16
## 8: 22.5 -0.12523757 24
## 9: 25.5 -0.13301969 24
## 10: 28.5 -0.11097038 24
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.68934 -0.16575 0.00973 0.19104 0.67014
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.157587 0.040226 3.918 9.92e-05 ***
## timeNorm -0.008747 0.037508 -0.233 0.816
## obj.diff -0.364236 0.054058 -6.738 3.61e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06744585)
##
## Null deviance: 45.961 on 637 degrees of freedom
## Residual deviance: 42.828 on 635 degrees of freedom
## AIC: 95.236
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5204545 0.6251419 -0.096882522 44 0.034 *
## 2: 4.5 0.5454545 0.6224524 -0.069888163 66 0.042 *
## 3: 7.5 0.5212121 0.5482212 -0.022392160 66 0.54 :(
## 4: 10.5 0.5257576 0.5744464 -0.036347555 66 0.35 :(
## 5: 13.5 0.5348485 0.5455378 -0.006192686 66 0.85 :(
## 6: 16.5 0.5272727 0.5560045 -0.033252815 66 0.35 :(
## 7: 19.5 0.4712121 0.5704673 -0.107605826 66 0.0013 **
## 8: 22.5 0.4439394 0.5060978 -0.066259279 66 0.063 .
## 9: 25.5 0.4787879 0.4999714 -0.024063555 66 0.54 :(
## 10: 28.5 0.4787879 0.5016324 -0.029290994 66 0.31 :(
## time error.diff shapes
## 1: 1.5 -0.096882522 24
## 2: 4.5 -0.069888163 24
## 3: 7.5 -0.022392160 16
## 4: 10.5 -0.036347555 16
## 5: 13.5 -0.006192686 16
## 6: 16.5 -0.033252815 16
## 7: 19.5 -0.107605826 24
## 8: 22.5 -0.066259279 16
## 9: 25.5 -0.024063555 16
## 10: 28.5 -0.029290994 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTM[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.61934 -0.16018 0.01038 0.17385 0.53652
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11181 0.02651 4.217 2.78e-05 ***
## timeNorm 0.02800 0.02850 0.983 0.326
## obj.diff -0.19693 0.04083 -4.823 1.71e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.04519554)
##
## Null deviance: 35.197 on 753 degrees of freedom
## Residual deviance: 33.942 on 751 degrees of freedom
## AIC: -190.2
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5192308 0.5021701 0.020553607 52 0.56 :(
## 2: 4.5 0.4820513 0.4581003 0.028602593 78 0.28 :(
## 3: 7.5 0.4602564 0.4705078 -0.007131425 78 0.76 :(
## 4: 10.5 0.5089744 0.4361551 0.085310624 78 0.0021 **
## 5: 13.5 0.4474359 0.3993679 0.055711847 78 0.043 *
## 6: 16.5 0.4846154 0.4316421 0.056312672 78 0.036 *
## 7: 19.5 0.4717949 0.4386951 0.030866623 78 0.22 :(
## 8: 22.5 0.4551282 0.3910376 0.068335238 78 0.013 *
## 9: 25.5 0.4256410 0.3686849 0.059334781 78 0.014 *
## 10: 28.5 0.4051282 0.3329405 0.069444110 78 0.0055 **
## time error.diff shapes
## 1: 1.5 0.020553607 16
## 2: 4.5 0.028602593 16
## 3: 7.5 -0.007131425 16
## 4: 10.5 0.085310624 24
## 5: 13.5 0.055711847 24
## 6: 16.5 0.056312672 24
## 7: 19.5 0.030866623 16
## 8: 22.5 0.068335238 24
## 9: 25.5 0.059334781 24
## 10: 28.5 0.069444110 24
Per group, sensory task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.73821 -0.20661 -0.03259 0.20583 0.62369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.22002 0.04425 4.972 1.14e-06 ***
## timeNorm 0.03942 0.05278 0.747 0.456
## obj.diff -0.51835 0.04440 -11.675 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06277719)
##
## Null deviance: 26.645 on 289 degrees of freedom
## Residual deviance: 18.017 on 287 degrees of freedom
## AIC: 25.201
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5200000 0.6390463 -0.13867440 20 0.11 :(
## 2: 4.5 0.5233333 0.6686706 -0.14681000 30 0.023 *
## 3: 7.5 0.5600000 0.7179520 -0.16882939 30 0.0047 **
## 4: 10.5 0.6166667 0.7022945 -0.09167106 30 0.13 :(
## 5: 13.5 0.6300000 0.7355270 -0.09696383 30 0.047 *
## 6: 16.5 0.5033333 0.6316433 -0.17116360 30 0.026 *
## 7: 19.5 0.5666667 0.6735104 -0.14469214 30 0.061 .
## 8: 22.5 0.6766667 0.7285240 -0.04571003 30 0.52 :(
## 9: 25.5 0.5200000 0.6387517 -0.10658266 30 0.07 .
## 10: 28.5 0.5400000 0.6238117 -0.06667086 30 0.26 :(
## time error.diff shapes
## 1: 1.5 -0.13867440 16
## 2: 4.5 -0.14681000 24
## 3: 7.5 -0.16882939 24
## 4: 10.5 -0.09167106 16
## 5: 13.5 -0.09696383 24
## 6: 16.5 -0.17116360 24
## 7: 19.5 -0.14469214 16
## 8: 22.5 -0.04571003 16
## 9: 25.5 -0.10658266 16
## 10: 28.5 -0.06667086 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.72208 -0.20591 0.01923 0.20060 0.76828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.11070 0.02506 4.417 1.14e-05 ***
## timeNorm 0.03910 0.03334 1.173 0.241
## obj.diff -0.31529 0.02603 -12.114 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06768712)
##
## Null deviance: 62.835 on 782 degrees of freedom
## Residual deviance: 52.796 on 780 degrees of freedom
## AIC: 118.54
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5185185 0.5856813 -0.077995802 54 0.068 .
## 2: 4.5 0.5839506 0.6540986 -0.051214048 81 0.0094 **
## 3: 7.5 0.4753086 0.4885319 -0.021685483 81 0.5 :(
## 4: 10.5 0.5246914 0.5978029 -0.068227022 81 0.038 *
## 5: 13.5 0.4975309 0.5802643 -0.074552535 81 0.0055 **
## 6: 16.5 0.4765432 0.5255582 -0.048653420 81 0.12 :(
## 7: 19.5 0.5222222 0.5760814 -0.029460170 81 0.19 :(
## 8: 22.5 0.4962963 0.5370262 -0.035337710 81 0.12 :(
## 9: 25.5 0.5827160 0.5877937 -0.003735285 81 0.87 :(
## 10: 28.5 0.5555556 0.5957763 -0.046408481 81 0.12 :(
## time error.diff shapes
## 1: 1.5 -0.077995802 16
## 2: 4.5 -0.051214048 24
## 3: 7.5 -0.021685483 16
## 4: 10.5 -0.068227022 24
## 5: 13.5 -0.074552535 24
## 6: 16.5 -0.048653420 16
## 7: 19.5 -0.029460170 16
## 8: 22.5 -0.035337710 16
## 9: 25.5 -0.003735285 16
## 10: 28.5 -0.046408481 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTS[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.68106 -0.15992 -0.08839 0.24816 0.75224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.07430 0.02812 2.642 0.00847 **
## timeNorm 0.03609 0.03932 0.918 0.35921
## obj.diff -0.38936 0.02997 -12.991 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06627138)
##
## Null deviance: 47.552 on 550 degrees of freedom
## Residual deviance: 36.317 on 548 degrees of freedom
## AIC: 73.25
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3736842 0.5824937 -0.22105237 38 0.00032 ***
## 2: 4.5 0.4052632 0.5178656 -0.09997599 57 0.0035 **
## 3: 7.5 0.4052632 0.4897451 -0.07737910 57 0.037 *
## 4: 10.5 0.4473684 0.4975966 -0.05160426 57 0.089 .
## 5: 13.5 0.3684211 0.4554127 -0.07786792 57 0.016 *
## 6: 16.5 0.3385965 0.4926908 -0.16375666 57 9.9e-05 ***
## 7: 19.5 0.3964912 0.4896047 -0.07782690 57 0.006 **
## 8: 22.5 0.4052632 0.5206631 -0.10748829 57 0.0074 **
## 9: 25.5 0.4877193 0.5600315 -0.04713849 57 0.1 :(
## 10: 28.5 0.4052632 0.5082965 -0.10381377 57 0.0058 **
## time error.diff shapes
## 1: 1.5 -0.22105237 24
## 2: 4.5 -0.09997599 24
## 3: 7.5 -0.07737910 24
## 4: 10.5 -0.05160426 16
## 5: 13.5 -0.07786792 24
## 6: 16.5 -0.16375666 24
## 7: 19.5 -0.07782690 24
## 8: 22.5 -0.10748829 24
## 9: 25.5 -0.04713849 16
## 10: 28.5 -0.10381377 24
Per group, logical task
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "bad"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.71273 -0.14992 -0.08786 0.27418 0.49321
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42032 0.06182 6.799 4.66e-11 ***
## timeNorm 0.10387 0.05362 1.937 0.0535 .
## obj.diff -0.79834 0.06101 -13.085 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.07238212)
##
## Null deviance: 39.631 on 347 degrees of freedom
## Residual deviance: 24.972 on 345 degrees of freedom
## AIC: 78.791
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.5375000 0.8667296 -0.336223188 24 8.3e-07 ***
## 2: 4.5 0.5666667 0.8503630 -0.300166002 36 8.1e-06 ***
## 3: 7.5 0.6500000 0.8149909 -0.191351300 36 0.0067 **
## 4: 10.5 0.6500000 0.7230354 -0.075959102 36 0.21 :(
## 5: 13.5 0.6277778 0.7492306 -0.168314672 36 0.043 *
## 6: 16.5 0.6944444 0.7843872 -0.103751069 36 0.1 :(
## 7: 19.5 0.6694444 0.7466016 -0.071855787 36 0.088 .
## 8: 22.5 0.5527778 0.7010553 -0.155156353 36 0.017 *
## 9: 25.5 0.6305556 0.6675804 -0.011808727 36 0.88 :(
## 10: 28.5 0.6638889 0.6510792 0.008180311 36 0.87 :(
## time error.diff shapes
## 1: 1.5 -0.336223188 24
## 2: 4.5 -0.300166002 24
## 3: 7.5 -0.191351300 24
## 4: 10.5 -0.075959102 16
## 5: 13.5 -0.168314672 24
## 6: 16.5 -0.103751069 16
## 7: 19.5 -0.071855787 16
## 8: 22.5 -0.155156353 24
## 9: 25.5 -0.011808727 16
## 10: 28.5 0.008180311 16## Warning: Removed 2 rows containing missing values (geom_errorbar).
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "medium"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.64791 -0.12252 -0.01687 0.08254 0.56550
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.412353 0.037259 11.067 <2e-16 ***
## timeNorm -0.004306 0.043597 -0.099 0.921
## obj.diff -0.758214 0.039233 -19.326 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.0555671)
##
## Null deviance: 44.748 on 405 degrees of freedom
## Residual deviance: 22.394 on 403 degrees of freedom
## AIC: -16.24
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.4571429 0.5900756 -0.128331978 28 0.074 .
## 2: 4.5 0.5785714 0.7531825 -0.175486568 42 0.00038 ***
## 3: 7.5 0.5571429 0.5622264 -0.024952321 42 0.62 :(
## 4: 10.5 0.6166667 0.5363606 0.074535928 42 0.26 :(
## 5: 13.5 0.5857143 0.5877105 -0.004272840 42 0.89 :(
## 6: 16.5 0.5761905 0.5680560 -0.005940806 42 0.91 :(
## 7: 19.5 0.4833333 0.5083317 -0.026640700 42 0.56 :(
## 8: 22.5 0.5333333 0.4752222 0.063331692 42 0.3 :(
## 9: 25.5 0.5238095 0.4415357 0.079086798 42 0.13 :(
## 10: 28.5 0.4595238 0.3652122 0.102052175 42 0.089 .
## time error.diff shapes
## 1: 1.5 -0.128331978 16
## 2: 4.5 -0.175486568 24
## 3: 7.5 -0.024952321 16
## 4: 10.5 0.074535928 16
## 5: 13.5 -0.004272840 16
## 6: 16.5 -0.005940806 16
## 7: 19.5 -0.026640700 16
## 8: 22.5 0.063331692 16
## 9: 25.5 0.079086798 16
## 10: 28.5 0.102052175 16
##
## Call:
## glm(formula = error.subj.diff.confiance ~ timeNorm + obj.diff,
## data = DTL[niveau.group == "good"])
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.65768 -0.19763 -0.04035 0.21009 0.72371
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12696 0.02761 4.598 5e-06 ***
## timeNorm 0.06463 0.03613 1.789 0.074 .
## obj.diff -0.37517 0.03533 -10.619 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.06922719)
##
## Null deviance: 61.766 on 753 degrees of freedom
## Residual deviance: 51.990 on 751 degrees of freedom
## AIC: 131.3
##
## Number of Fisher Scoring iterations: 2## time.bin subj.diff.mean obj.diff.mean error.diff n pval
## 1: 1.5 0.3769231 0.4760639 -0.102437221 52 0.042 *
## 2: 4.5 0.4448718 0.4614922 -0.027765478 78 0.44 :(
## 3: 7.5 0.4205128 0.4300504 -0.017443021 78 0.62 :(
## 4: 10.5 0.4128205 0.4234581 -0.008671569 78 0.81 :(
## 5: 13.5 0.4294872 0.4001833 0.034151496 78 0.44 :(
## 6: 16.5 0.3897436 0.3337317 0.052219011 78 0.13 :(
## 7: 19.5 0.3461538 0.2730373 0.067485507 78 0.073 .
## 8: 22.5 0.3000000 0.2602781 0.014460765 78 0.59 :(
## 9: 25.5 0.3423077 0.2469083 0.092629756 78 0.011 *
## 10: 28.5 0.3333333 0.2303798 0.075968753 78 0.048 *
## time error.diff shapes
## 1: 1.5 -0.102437221 24
## 2: 4.5 -0.027765478 16
## 3: 7.5 -0.017443021 16
## 4: 10.5 -0.008671569 16
## 5: 13.5 0.034151496 16
## 6: 16.5 0.052219011 16
## 7: 19.5 0.067485507 16
## 8: 22.5 0.014460765 16
## 9: 25.5 0.092629756 24
## 10: 28.5 0.075968753 24
Link between d(t-1) and error
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTM)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.86921 -0.17599 0.01494 0.18377 0.73119
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05604 0.01198 4.679 3.13e-06 ***
## est.confidence.norm -0.15362 0.02116 -7.260 5.99e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.05923049)
##
## Null deviance: 99.193 on 1623 degrees of freedom
## Residual deviance: 96.072 on 1622 degrees of freedom
## AIC: 22.769
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTS)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.92482 -0.19376 0.02463 0.13088 0.94828
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.08982 0.01468 -6.119 1.17e-09 ***
## est.confidence.norm 0.02209 0.02543 0.869 0.385
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.08527575)
##
## Null deviance: 138.38 on 1623 degrees of freedom
## Residual deviance: 138.32 on 1622 degrees of freedom
## AIC: 614.64
##
## Number of Fisher Scoring iterations: 2
##
## Call:
## glm(formula = error.subj.diff.confiance ~ est.confidence.norm,
## data = DTL)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.98483 -0.21113 -0.02991 0.20880 0.95656
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05668 0.01645 3.445 0.000587 ***
## est.confidence.norm -0.13431 0.02769 -4.851 1.36e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.09936574)
##
## Null deviance: 151.98 on 1507 degrees of freedom
## Residual deviance: 149.64 on 1506 degrees of freedom
## AIC: 801.62
##
## Number of Fisher Scoring iterations: 2## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4952 -0.6559 -0.0029 0.6255 3.3993
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.009097 0.09538
## Residual 0.073378 0.27088
## Number of obs: 4756, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01374 0.01529 102.00000 -0.899 0.37080
## est.confidence.norm -0.04492 0.01559 4634.00000 -2.881 0.00399 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.512
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -649.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1893 -0.6847 -0.0450 0.6981 3.1667
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.0250 0.1581
## Residual 0.0351 0.1873
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.02108 0.02542 97.30000 0.829 0.40895
## est.confidence.norm -0.08211 0.02731 1489.90000 -3.007 0.00268 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.525
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 498.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0656 -0.6965 0.0514 0.5899 3.6071
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01108 0.1052
## Residual 0.07470 0.2733
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11873 0.02296 154.00000 -5.171 7.13e-07 ***
## est.confidence.norm 0.07968 0.03353 739.10000 2.376 0.0177 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.733
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 649.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9645 -0.6521 -0.0602 0.6569 3.2634
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01912 0.1383
## Residual 0.08347 0.2889
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03688 0.02801 120.70000 -1.317 0.190
## est.confidence.norm 0.04670 0.03679 946.90000 1.270 0.205
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.679## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTAll
##
## REML criterion at convergence: 1224.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4952 -0.6559 -0.0029 0.6255 3.3993
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.009097 0.09538
## Residual 0.073378 0.27088
## Number of obs: 4756, groups: IDjoueur, 58
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.01374 0.01529 102.00000 -0.899 0.37080
## est.confidence.norm -0.04492 0.01559 4634.00000 -2.881 0.00399 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.512
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTM
##
## REML criterion at convergence: -649.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1893 -0.6847 -0.0450 0.6981 3.1667
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.0250 0.1581
## Residual 0.0351 0.1873
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.02108 0.02542 97.30000 0.829 0.40895
## est.confidence.norm -0.08211 0.02731 1489.90000 -3.007 0.00268 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.525
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTS
##
## REML criterion at convergence: 498.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0656 -0.6965 0.0514 0.5899 3.6071
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01108 0.1052
## Residual 0.07470 0.2733
## Number of obs: 1624, groups: IDjoueur, 56
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.11873 0.02296 154.00000 -5.171 7.13e-07 ***
## est.confidence.norm 0.07968 0.03353 739.10000 2.376 0.0177 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.733
## Linear mixed model fit by REML t-tests use Satterthwaite approximations
## to degrees of freedom [lmerMod]
## Formula: error.subj.diff.confiance ~ est.confidence.norm + (1 | IDjoueur)
## Data: DTL
##
## REML criterion at convergence: 649.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9645 -0.6521 -0.0602 0.6569 3.2634
##
## Random effects:
## Groups Name Variance Std.Dev.
## IDjoueur (Intercept) 0.01912 0.1383
## Residual 0.08347 0.2889
## Number of obs: 1508, groups: IDjoueur, 52
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.03688 0.02801 120.70000 -1.317 0.190
## est.confidence.norm 0.04670 0.03679 946.90000 1.270 0.205
##
## Correlation of Fixed Effects:
## (Intr)
## est.cnfdnc. -0.679Subjective difficulty and play habits
Playing video game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -0.52035, p-value = 0.6028
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.03154256Playing board game in general and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.5031, p-value = 0.01231
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1499913
##
## [1] "pbg.on.error -0.15 0.012 *"In game level and subjective difficulty error
## [1] "niveau.group.on.error 0.035 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.3837, p-value = 0.01714
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1261215
##
## [1] "niveau.group.on.error 0.13 0.017 *"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.046, p-value = 0.2956
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.0961039
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.7103, p-value = 0.0872
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1571429
##
## [1] "niveau.group.on.error.s 0.16 0.087 ."
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.6256, p-value = 0.104
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1553544Sex and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 2.7398, p-value = 0.006148
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1767461
##
## [1] "sexe.on.error 0.18 0.0061 **"
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.4211, p-value = 0.1553
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1578435
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.4796, p-value = 0.139
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.1643456
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 1.8465, p-value = 0.06482
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.2131345
##
## [1] "sexe.on.error.l 0.21 0.065 ."
##
## Wilcoxon rank sum test with continuity correction
##
## data: B and A
## W = 3583, p-value = 0.04157
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## 0.0003401476 0.0699788354
## sample estimates:
## difference in location
## 0.03729529
##
## [1] "sexe.on.error.2 0.037 0.042 * mean(A): -0.053 mean(B): -0.01"
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 400, p-value = 0.3162
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.02793954 0.09620850
## sample estimates:
## difference in location
## 0.0346001
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 418, p-value = 0.256
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.02621098 0.10067023
## sample estimates:
## difference in location
## 0.03723874
##
## Wilcoxon rank sum test
##
## data: B and A
## W = 377, p-value = 0.1768
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
## -0.01807186 0.10421161
## sample estimates:
## difference in location
## 0.04388372Risk aversion and subjective difficulty error
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.99564, p-value = 0.3194
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05721568
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.47909, p-value = 0.6319
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.04749357
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.56668, p-value = 0.5709
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.05612876
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = 0.72205, p-value = 0.4703
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## 0.07447498Self efficacy and subjective difficulty error
## Warning: Removed 82 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.7073, p-value = 0.006783
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2123197
##
## [1] "self.eff.on.error -0.21 0.0068 **"## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 28 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.4877, p-value = 0.1368
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2024706## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning in cor.test.default(Y, X, method = "kendall"): Removed 28 rows
## containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -1.1324, p-value = 0.2575
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.1547326## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties## Warning: Removed 26 rows containing missing values (geom_point).
##
## Kendall's rank correlation tau
##
## data: Y and X
## z = -2.0192, p-value = 0.04346
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.2880916
##
## [1] "self.eff.on.error -0.29 0.043 *"